The present work focuses on the modeling and analysis of mechanical properties of structural steel. The effect of major alloying elements

namely carbon, manganese and silicon has been investigated on mechanical properties of structural steel. Design of experiments is used to

develop linear models for the responses namely Yield strength, Ultimate tensile strength and Elongation. The experiments have been

conducted as per the full factorial design where all process variables are set at two levels. The main effect plots showed that the alloying

elements Manganese and Silicon have positive contribution on Ultimate tensile strength and Yield strength. However, Carbon and

Manganese showed more contribution as compared to Silicon. All three alloying elements are found to have negative contribution

towards the response- Elongation. The present work is found to be useful to control the mechanical properties of structural steel by varying

the major alloying elements. Minitab software has been used for statistical analysis. The linear regression models have been tested for the

statistical adequacy by utilizing ANOVA and statistical significance test. Further, the prediction capability of the developed models is

tested with the help of test cases. It is found that all linear regression models are found to be statistically adequate with good prediction

capability. The work is useful to foundrymen to choose alloying elements composition to get desirable mechanical properties.

The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input

parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze

pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output

relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input

parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based

approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy

system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the

performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models

were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of

a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations.

The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will

help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource

consuming.

Chemical bonded resin sand mould system has high dimensional accuracy, surface finish and sand mould properties compared to green

sand mould system. The mould cavity prepared under chemical bonded sand mould system must produce sufficient permeability and

hardness to withstand sand drop while pouring molten metal through ladle. The demand for improved values of permeability and mould

hardness depends on systematic study and analysis of influencing variables namely grain fineness number, setting time, percent of resin

and hardener. Try-error experiment methods and analysis were considered impractical in actual foundry practice due to the associated cost.

Experimental matrices of central composite design allow conducting minimum experiments that provide complete insight of the process.

Statistical significance of influencing variables and their interaction were determined to control the process. Analysis of variance

(ANOVA) test was conducted to validate the model statistically. Mathematical equation was derived separately for mould hardness and

permeability, which are expressed as a non-linear function of input variables based on the collected experimental input-output data. The

developed model prediction accuracy for practical usefulness was tested with 10 random experimental conditions. The decision variables

for higher mould hardness and permeability were determined using desirability function approach. The prediction results were found to be

consistent with experimental values.

The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal

levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal

with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary

manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process

variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the

responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present

manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO)

and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple

outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and

MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.